Does where you live influence your socio-economic status?. (March 2018)

• Record Type: Journal Article
• Authors: Lockwood, Tony; Coffee, Neil T; Rossini, Peter; Niyonsenga, Theo; McGreal, Stanley
• Is Part Of: Land use policy. Volume 72(2018)
• Page Start: 152
• Page End: 160
• Publication Date: 2018-03
• Subjects: Residential property -- Spatial analysis -- Socio-economic status -- Societal wellbeing -- GIS; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2017.12.045
• Languages: English
• ISSNs: 0264-8377

Abstract

Abstract: The relationship between the wellbeing of society and understanding of land market structure and behaviour is an important research theme for understanding socioeconomic status (SES). Traditional SES area based measures of income, occupation and education are generally applied in the examination of a broad spectrum of societal issues. This paper examines the contribution of understanding the spatial variation of SES based upon residential property sales data unrestricted by the traditional artificial geographic boundaries in which SES is assumed uniform. Originality lies in identifying the locational component of residential property wealth as a proxy for SES. It includes market behavioural characteristics that reflect both the context and composition at particular locations. This provides a broader understanding of SES than income, occupation and education. The analysis uses a hedonic regression model based on transactions of detached housing. The model is specified using only available property attributes as independent variables and is therefore blind to location.. The residuals from this hedonic model are used to calculate the relative location factor (RLF) for each transaction property. These were interpolated as a continuous surface capable of predicting values at the individual property level or aggregated to a spatial unit relevant to the particular application. There was a significant correlation with the traditional SES indicators and health outcomes that have traditionally been shown to have a correlation with SES.